Keyphrase Extraction Using Neighborhood Knowledge Based on Word Embeddings

• URL: http://arxiv.org/abs/2111.07198v1
• Date: Sat, 13 Nov 2021 21:48:18 GMT
• Title: Keyphrase Extraction Using Neighborhood Knowledge Based on Word Embeddings
• Authors: Yuchen Liang and Mohammed J. Zaki
• Abstract summary: We enhance the graph-based ranking model by leveraging word embeddings as background knowledge to add semantic information to the inter-word graph. Our approach is evaluated on established benchmark datasets and empirical results show that the word embedding neighborhood information improves the model performance.
• Score: 17.198907789163123
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Keyphrase extraction is the task of finding several interesting phrases in a text document, which provide a list of the main topics within the document. Most existing graph-based models use co-occurrence links as cohesion indicators to model the relationship of syntactic elements. However, a word may have different forms of expression within the document, and may have several synonyms as well. Simply using co-occurrence information cannot capture this information. In this paper, we enhance the graph-based ranking model by leveraging word embeddings as background knowledge to add semantic information to the inter-word graph. Our approach is evaluated on established benchmark datasets and empirical results show that the word embedding neighborhood information improves the model performance.

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